
We have developed a tree-structured mutation operation for GA to adaptively maintain the balance between exploration and exploitation for searching the hyperparameter space of GNNs while retaining the randomness of mutation operator.

We conducted a review of advanced HPO methods, and we found that the tree-structured approach has the potential to be integrated into other approaches.

Our research contributes to the development of molecular machine learning [1]} as well as HPO for GNNs in general.

